耦合ER和GWR的福州市生态环境质量的驱动力分析

doi:10.16258/j.cnki.1674-5906.2024.05.014

生态环境学报 ›› 2024, Vol. 33 ›› Issue (5): 812-823.DOI: 10.16258/j.cnki.1674-5906.2024.05.014

• 研究论文【环境科学】 • 上一篇下一篇

耦合ER和GWR的福州市生态环境质量的驱动力分析

陈晓辉¹(), 胡喜生²^,^*()

1.漳州职业技术学院智能制造学院，福建漳州 363000
2.福建农林大学交通与土木工程学院，福建福州 350100

收稿日期:2023-11-09 出版日期:2024-05-18 发布日期:2024-06-27
通讯作者: * 胡喜生。E-mail: xshu@fafu.edu.cn
作者简介:陈晓辉（1997年生），男，助教，硕士，主要研究方向为道路交通环境影响评价。E-mail: ChenXiaoHui_home@163.com
基金资助:
国家自然科学基金项目(31971639);福建省自然科学基金项目(2023J01477);漳州职业技术学院智能装备与关键技术科研创新团队项目

Analysis of the Driving Forces of Ecological Environment Quality in Fuzhou City Coupled with ER and GWR

CHEN Xiaohui¹(), HU Xisheng²^,^*()

1. College of Intelligent Manufacturing, Zhangzhou Institute of Technology, Zhangzhou, 363000, P. R. China
2. College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350100, P. R. China

Received:2023-11-09 Online:2024-05-18 Published:2024-06-27

摘要/Abstract

摘要：

城市的生态环境质量受到诸多因素的共同影响，其中以道路建设最为显著，客观分析道路网络及其他因素对生态环境质量的驱动机制，对最大限度减少对生态系统的负面作用具有重要的参考意义。基于道路网络、Landsat系列遥感影像、夜间灯光、数字高程模型、气象和土地利用等多源数据集，在3S技术的支持下，首先采用增量空间自相关和核密度估算（KDE）计算福州市2015年和2020年的道路核密度，利用主成分分析法构建福州市2000、2009和2020年的遥感生态指数（RSEI），在此基础上，分析两者的时空动态变化，接着采用探索性回归（ER）筛选关键影响因子，最后运用地理加权回归模型（GWR）揭示关键影响因子对福州市生态环境质量的驱动机制。结果表明，1）2015年和2020年最佳带宽下KDE的变化范围分别是0－4.090 km∙km⁻²和0－3.765 km∙km⁻²；高KDE值在2015年主要聚集在福州市区周围和各区县的中心，而至2020年，高KDE值的范围逐渐扩大，呈现向沿海地区蔓延的趋势。2）从时间上看，福州市2000－2020年期间的RSEI呈现先上升后下降的趋势，生态状况整体上相对稳定；从空间上看，生态环境质量好的等级分布在永泰县和闽清县等山区，较差的等级主要分布在市区中心、各区县的中心区、东部沿海区域及沿江两侧。3）对探索性回归筛选出的最佳因子进行拟合，GWR模型拟合效果优于OLS模型。GWR结果表明道路欧氏距离、高程、坡度、林地比例和草地比例与RSEI主要呈正相关关系，道路核密度、夜间灯光、城乡用地比例与RSEI主要呈负相关关系，回归系数的分布呈现明显的空间分异特征。研究结果可为福州市以及其他城市路网规划和生态环境质量提升提供参考依据。

关键词: 道路核密度, 遥感生态指数, 时空变化, 探索性回归, 地理加权回归

Abstract:

The ecological quality of urban environments is jointly influenced by multiple factors, with road construction being the most significant. Objectively analyzing the driving mechanisms of road networks and other factors on ecological quality is of great importance for minimizing the negative impacts on ecosystems. Based on multi-source datasets including road networks, Landsat series remote sensing images, nighttime lights, digital elevation models, meteorology, and land use, the road density in Fuzhou City in 2015 and 2020 was calculated by using incremental spatial autocorrelation and kernel density estimation (KDE) supported by 3S technology. Then, the remote sensing ecological index (RSEI) for Fuzhou City in 2000, 2009, and 2020 was constructed using principal component analysis. On this basis, the spatiotemporal dynamic changes of road density and RSEI were analyzed. Exploratory regression (ER) was then used to identify key influencing factors. Finally, a geographically weighted regression model (GWR) was applied to reveal the driving mechanisms of key influencing factors on the ecological environment quality in Fuzhou City. The results indicate that 1) the range of KDE under the optimal bandwidth in 2015 and 2020 was 0-4.090 km∙km⁻² and 0-3.765 km∙km⁻², respectively. In 2015, high KDE values were mainly concentrated around the urban area of Fuzhou and the centers of various districts and counties. However, by 2020, the range of high KDE values had gradually expanded, showing a tendency to spread towards coastal areas. 2) From a temporal perspective, the RSEI of Fuzhou City showed an upward trend followed by a downward trend from 2000 to 2020, indicating a relatively stable ecological condition overall. Spatially, areas with good ecological environment quality were mainly distributed in mountainous regions such as Yongtai County and Minqing County. Areas with poorer quality were primarily concentrated in the central urban area, central areas of various districts and counties, eastern coastal regions, and along both sides of the river. 3) The GWR model, fitted with the best factors selected through ER, outperformed the OLS model. The GWR results showed that the Euclidean distance, elevation, slope, forest proportion, and grassland proportion were positively correlated with RSEI. Road density, nighttime lights, and urban-rural land ratio were negatively correlated with RSEI. The distribution of regression coefficients exhibited significant spatial variations. These findings can provide meaningful implications for road network planning and improvement of ecological environment quality in Fuzhou and other cities.

Key words: road kernel density, remote sensing ecological index, spatio-temporal changes, exploratory regression, geographically weighted regression

中图分类号:

陈晓辉, 胡喜生. 耦合ER和GWR的福州市生态环境质量的驱动力分析[J]. 生态环境学报, 2024, 33(5): 812-823.

CHEN Xiaohui, HU Xisheng. Analysis of the Driving Forces of Ecological Environment Quality in Fuzhou City Coupled with ER and GWR[J]. Ecology and Environment, 2024, 33(5): 812-823.

图/表 9

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数据类型	数据介绍	分辨率	数据来源
道路数据	道路网络空间分布矢量数据	‒	Open Street Map开源街道地图数据集(http://www.geofabrik.de/geofabrik/openstreetmap.html)
Landsat数据	Landsat5TM (成像时间: 2000-06-29, 云量: 0.82%; 成像时间: 2009-06-06, 云量: 0.06%), Landsat8OLI (成像时间: 2020-07-22, 云量: 11.77%)	30 m×30 m	地理空间数据云 (http://www.gscloud.cn)
矢量数据	福建省行政区划边界	‒	国家基础地理信息中心(http://www.ngcc.cn/ngcc)
夜间灯光数据	“类NPP-VIIRS” 数据	1 km×1 km	国家地球系统科学数据中心(http://www.geodata.cn)
DEM数据	SRTM数据	30 m×1 km	美国国家航空航天局陆地过程分布式活动档案中心的数字资源镜像网站(https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_HGT.001)
气象数据	逐月平均气温数据和逐月降水量数据	1 km×1 km	国家地球系统科学数据中心 (http://www.geodata.cn)
土地利用数据	6种一级地类土地覆盖数据	30 m×30 m	中科院资源环境科学数据中心 (https://www.resdc.cn)

数据类型	数据介绍	分辨率	数据来源
道路数据	道路网络空间分布矢量数据	‒	Open Street Map开源街道地图数据集(http://www.geofabrik.de/geofabrik/openstreetmap.html)
Landsat数据	Landsat5TM (成像时间: 2000-06-29, 云量: 0.82%; 成像时间: 2009-06-06, 云量: 0.06%), Landsat8OLI (成像时间: 2020-07-22, 云量: 11.77%)	30 m×30 m	地理空间数据云 (http://www.gscloud.cn)
矢量数据	福建省行政区划边界	‒	国家基础地理信息中心(http://www.ngcc.cn/ngcc)
夜间灯光数据	“类NPP-VIIRS” 数据	1 km×1 km	国家地球系统科学数据中心(http://www.geodata.cn)
DEM数据	SRTM数据	30 m×1 km	美国国家航空航天局陆地过程分布式活动档案中心的数字资源镜像网站(https://e4ftl01.cr.usgs.gov/MEASURES/NASADEM_HGT.001)
气象数据	逐月平均气温数据和逐月降水量数据	1 km×1 km	国家地球系统科学数据中心 (http://www.geodata.cn)
土地利用数据	6种一级地类土地覆盖数据	30 m×30 m	中科院资源环境科学数据中心 (https://www.resdc.cn)

指标	RSEI	KDE	ED	NL	DEM	SA	SLOP	AAT	AAR	CLP	FLP	GLP	URLP	ULP
RSEI	1	−0.450** ²⁾	0.386**	−0.513**	0.545**	0.083**	0.526**	−0.512**	0.438**	−0.469**	0.621**	0.084**	−0.552**	−0.017
KDE		1	−0.397**	0.767**	−0.458**	−0.018	−0.328**	0.453**	−0.344**	0.179**	−0.433**	−0.105**	0.646**	−0.008
ED			1	−0.286**	0.445**	0.004	0.321**	−0.386**	0.207**	−0.329**	0.388**	0.031**	−0.272**	−0.002
NL				1	−0.410**	−0.035**	−0.352**	0.402**	−0.326**	0.166**	−0.481**	−0.121**	0.772**	−0.011
DEM					1	0.031**	0.356**	−0.917**	0.723**	−0.423**	0.493**	0.100**	−0.386**	0.003
SA						1	0.076**	−0.013	0.020*¹⁾	−0.087**	0.104**	−0.017	−0.046**	−0.013
SLOP							1	−0.317**	0.269**	−0.477**	0.509**	0.102**	−0.356**	−0.009
AAT								1	−0.825**	0.378**	−0.456**	−0.089**	0.371**	−0.01
AAR									1	−0.334**	0.380**	0.090**	−0.295**	0.021*
CLP										1	−0.674**	−0.227**	0.109**	−0.007
FLP											1	−0.321**	−0.528**	−0.012
GLP												1	−0.150**	0.001
URLP													1	0.016
ULP														1

指标	RSEI	KDE	ED	NL	DEM	SA	SLOP	AAT	AAR	CLP	FLP	GLP	URLP	ULP
RSEI	1	−0.450** ²⁾	0.386**	−0.513**	0.545**	0.083**	0.526**	−0.512**	0.438**	−0.469**	0.621**	0.084**	−0.552**	−0.017
KDE		1	−0.397**	0.767**	−0.458**	−0.018	−0.328**	0.453**	−0.344**	0.179**	−0.433**	−0.105**	0.646**	−0.008
ED			1	−0.286**	0.445**	0.004	0.321**	−0.386**	0.207**	−0.329**	0.388**	0.031**	−0.272**	−0.002
NL				1	−0.410**	−0.035**	−0.352**	0.402**	−0.326**	0.166**	−0.481**	−0.121**	0.772**	−0.011
DEM					1	0.031**	0.356**	−0.917**	0.723**	−0.423**	0.493**	0.100**	−0.386**	0.003
SA						1	0.076**	−0.013	0.020*¹⁾	−0.087**	0.104**	−0.017	−0.046**	−0.013
SLOP							1	−0.317**	0.269**	−0.477**	0.509**	0.102**	−0.356**	−0.009
AAT								1	−0.825**	0.378**	−0.456**	−0.089**	0.371**	−0.01
AAR									1	−0.334**	0.380**	0.090**	−0.295**	0.021*
CLP										1	−0.674**	−0.227**	0.109**	−0.007
FLP											1	−0.321**	−0.528**	−0.012
GLP												1	−0.150**	0.001
URLP													1	0.016
ULP														1

解释变量数	影响因子组合模型	调整的r²	AIC	VIF
5	+SLOP*, +FLP, +GLP, −URLP, +DEM**	0.55	−13526.69	2.76
	+SLOP*, +CLP, +FLP, +GLP, +DEM**	0.54	−13486.63	4.39
	+SLOP*, −CLP, +FLP, −URLP, +DEM**	0.55	−13457.75	3.03
6	+SLOP*, +AAR, +FLP, +GLP, −URLP, +DEM*	0.55	−13564.46	2.77
	+ED*, +SLOP, +FLP, +GLP, −URLP, +DEM*	0.55	−13563.09	2.87
	−NL*, +SLOP, +FLP, +GLP, −URLP, +DEM*	0.55	−13652.34	2.87
7	+ED*, +SLOP, +AAR, +FLP, +GLP, −URLP, +DEM**	0.56	−13631.16	2.88
	−NL*, +SLOP, +AAR, +FLP, +GLP, −URLP, +DEM**	0.56	−13687.69	2.88
	+ED*, −NL, +SLOP, +FLP, +GLP, −URLP, +DEM**	0.56	−13686.59	2.88
8	+ED*, −NL, +SLOP, +AAR, +FLP, +GLP, −URLP, +DEM*	0.56	−13641.24	2.88
8	−KDE*, +ED, −NL, +DEM, +SLOP, +FLP, +GLP, −URLP*	0.56	−13621.03	2.83
9	−KDE*, +ED, −NL, +SLOP, +AAR, +FLP, +GLP, −URLP, +DEM**	0.56	−13672.98	3.62
9	+ED*, −NL, +SA, +SLOP, +AAR, +FLP, +GLP, −URLP, +DEM**	0.56	−13649.47	2.88
10	−KDE*, +ED, −NL, +SA, +SLOP, +AAR, +FLP, +GLP, −URLP, +DEM*	0.56	−13680.74	3.62
10	−KDE*, +ED, −NL, +SLOP, +AAR, +FLP, +GLP, −URLP, −ULP, +DEM**	0.56	−13673.94	3.63
11	−KDE*, +ED, −NL, +SA, +SLOP, +AAR, +FLP, +GLP, −URLP, −ULP, +DEM*	0.56	−13681.58	3.63

耦合ER和GWR的福州市生态环境质量的驱动力分析

Analysis of the Driving Forces of Ecological Environment Quality in Fuzhou City Coupled with ER and GWR

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模型		调整的r²	AIC	残差平方和	残差Moran’s I
2020年RSEI与KDE、ED、NL、DEM、SLOP、 SA、AAR、AAT、CLP、FLP、GLP、URLP	GWR	0.351	−15605	177.034	0.138
2020年RSEI与KDE、ED、NL、DEM、SLOP、 SA、AAR、AAT、CLP、FLP、GLP、URLP	OLS	0.298	−15987	169.389	0.213
2020年RSEI与KDE、ED、NL、 DEM、SLOP、FLP、GLP、URLP	GWR	0.594	−14308	147.025	0.024
2020年RSEI与KDE、ED、NL、 DEM、SLOP、FLP、GLP、URLP	OLS	0.558	−13587	166.215	0.091

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